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Though the README is probably enough context, would there be value in an agent skill for this? https://agentskills.io/what-are-skills https://agentskills.io/wh

Though the README is probably enough context, would there be value in an agent skill for this? https://agentskills.io/what-are-skills https://agentskills.io/what-are-skills SKILL.md format: https://agentskills.io/specification#skill-md-format https://agentskills.io/specification#skill-md-format The skill creator skill SKILL.md: https://github.com/anthropics/skills/blob/main/skills/skill-creator/SKILL.md https://github.com/anthropics/skills/blob/main/skills/skill-... . SWE bench involves solving GitHub issues. Which of the SWE bench agents read from or write to GitHub issues? . There could be a local cache of remote issues, and/or an overlayfs-like write cache From https://github.com/GothenburgBitFactory/bugwarrior https://github.com/GothenburgBitFactory/bugwarrior : > bugwarrior - Pull tickets from github, bitbucket, bugzilla, jira, trac, and others into taskwarrior https://github.com/bergercookie/syncall https://github.com/bergercookie/syncall : > Bi-directional synchronization between services such as Taskwarrior, Google Calendar, Notion, Asana, and more . ditz is on the filesystem ditz: https://github.com/jashmenn/ditz https://github.com/jashmenn/ditz : > Ditz is a simple, light-weight distributed issue tracker designed to work with distributed version control systems like git, darcs, Mercurial, and Bazaar git-ditz: https://github.com/ihrke/git-ditz https://github.com/ihrke/git-ditz . sqlite-utils does SQLite FTS full text search config. There are additional tables for FTS indexes. sqlite-utils: https://github.com/simonw/sqlite-utils https://github.com/simonw/sqlite-utils sqlite-utils > Configuring full-text search: https://sqlite-utils.datasette.io/en/stable/cli.html#configuring-full-text-search https://sqlite-utils.datasette.io/en/stable/cli.html#configu... : sqlite-utils enable-fts mydb.db documents title summary # after subsequent writes sqlite-utils populate-fts mydb.db documents title summary # rebuild the FTS indexes sqlite-utils rebuild-fts mydb.db documents . One of these mentions sensitivity analysis and basically cache poisoning; From "I built a memory system for Claude that solves the context loss issue" https://news.ycombinator.com/item?id=45546276 https://news.ycombinator.com/item?id=45546276 : - buildautomata_memory_mcp: https://github.com/brucepro/buildautomata_memory_mcp https://github.com/brucepro/buildautomata_memory_mcp Notes from "Show HN: Recall: Give Claude memory with Redis-backed persistent context" a few days ago: https://news.ycombinator.com/context?id=45517613 https://news.ycombinator.com/context?id=45517613 : - recall: https://github.com/joseairosa/recall https://github.com/joseairosa/recall sediment: https://github.com/rendro/sediment https://github.com/rendro/sediment : > Semantic memory for AI agents. Local-first, MCP-native sediment-benchmark: https://github.com/rendro/sediment-benchmark https://github.com/rendro/sediment-benchmark : > Benchmark suite for memory systems (Sediment, ChromaDB, Mem0, Letta)

spranab 8mo ago
Thanks for the excellent references! We actually just added a SKILL.md following the agentskills.io spec based on your suggestion — it's live now: https://github.com/spranab/saga-mcp/blob/master/.skills/saga-tracker/SKILL.md https://github.com/spranab/saga-mcp/blob/master/.skills/saga... Great point about sqlite-utils FTS. Saga already has full-text search via tracker_search across all entities (projects, epics, tasks, notes), but we're using basic LIKE queries — proper SQLite FTS5 indexes would be a meaningful upgrade for larger projects. Adding it to the roadmap. The bugwarrior/syncall direction is interesting too. A github_sync tool that pulls issues into Saga's local tracker (and optionally pushes status back) would close the loop between local agent planning and team-visible issue trackers. Definitely something worth exploring. Re: SWE-bench agents and issue tracking — most SWE-bench agents read the issue description as input but don't write back to GitHub. Having a local structured tracker that an agent can read/write freely (without API rate limits or auth concerns) is exactly the gap Saga fills.
- spranab 8mo ago
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- westurner 8mo ago
  Np. Still learning about how to reference agentskills from my coding LLM here. Haven't yet worked with MCP or A2A much. I should port instruction prompts from each project's AGENTS.md to reusable skills in .claude/skills/skillname/SKILL.md ; so that I can reference "/skillname" in a prompt to reuse that prompt text. Copilot looks for agent skills in .claude/skills/, .github/skills/, or .agents/skills/ . FWIU there are package managers for agent skills? PAKS: https://github.com/stakpak/paks https://github.com/stakpak/paks vercel-labs/skills: https://github.com/vercel-labs/skills https://github.com/vercel-labs/skills : export DISABLE_TELEMETRY=1 npx skills add vercel-labs/agent-skills https://skills.sh/docs/cli https://skills.sh/docs/cli A reproducibility, traceability, and auditability concern: How to show the text of the exact version of the skill referenced in each input prompt in each chat log? GH copilot's JSON export format is a fairly complete trace compared to e.g. Gemini Chat. . ElasticSearch (Java) and Meilisearch (Rust) do at least porter stemming before indexing. MeiliSearch can be used as a vector database for langchain. meilisearch/meilisearch: https://github.com/meilisearch/meilisearch https://github.com/meilisearch/meilisearch : Meilisearch docs > langchain guide > Performing Similarity Search: https://www.meilisearch.com/docs/guides/langchain#performing-similarity-search https://www.meilisearch.com/docs/guides/langchain#performing... from langchain.vectorstores import Meilisearch from langchain.embeddings.openai import OpenAIEmbeddings meilisearch/meilisearch-mcp : https://github.com/meilisearch/meilisearch-mcp https://github.com/meilisearch/meilisearch-mcp openobserve/openobserve : https://github.com/openobserve/openobserve https://github.com/openobserve/openobserve : > OpenObserve is an open-source observability platform for logs, metrics, traces, and frontend monitoring. A cost-effective alternative to Datadog, Splunk, and Elasticsearch with 140x lower storage costs and single binary deployment. with SQL in Rust OpenObserve (AGPL) docs > MCP: https://openobserve.ai/docs/integration/mcp/ https://openobserve.ai/docs/integration/mcp/ : > This capability is supported in the Enterprise edition of OpenObserve https://github.com/openobserve/openobserve/issues/6804 https://github.com/openobserve/openobserve/issues/6804 openobserve docs > Comparison with Alternatives > How does OpenObserve compare to Elasticsearch: https://openobserve.ai/docs/overview/comparison/ https://openobserve.ai/docs/overview/comparison/ meilisearch blog > "What is GraphRAG: Complete guide [2026]" https://www.meilisearch.com/blog/graph-rag https://www.meilisearch.com/blog/graph-rag : > How is GraphRAG different from baseline RAG? > GraphRAG represents one of the different types of RAG, and its retrieval process differs from baseline RAG. > Baseline RAG is vector search-based, while GraphRAG uses structured relationships to get the end result. GraphRAG can still use vector and full-text search, but relationships drive what gets retrieved. /? graphrag sqlite fts : https://www.google.com/search?q=graphrag+sqlite+fts https://www.google.com/search?q=graphrag+sqlite+fts

spranab 8mo ago

Thanks for the excellent references! We actually just added a SKILL.md following the agentskills.io spec based on your suggestion — it's live now: https://github.com/spranab/saga-mcp/blob/master/.skills/saga-tracker/SKILL.md https://github.com/spranab/saga-mcp/blob/master/.skills/saga... Great point about sqlite-utils FTS. Saga already has full-text search via tracker_search across all entities (projects, epics, tasks, notes), but we're using basic LIKE queries — proper SQLite FTS5 indexes would be a meaningful upgrade for larger projects. Adding it to the roadmap. The bugwarrior/syncall direction is interesting too. A github_sync tool that pulls issues into Saga's local tracker (and optionally pushes status back) would close the loop between local agent planning and team-visible issue trackers. Definitely something worth exploring. Re: SWE-bench agents and issue tracking — most SWE-bench agents read the issue description as input but don't write back to GitHub. Having a local structured tracker that an agent can read/write freely (without API rate limits or auth concerns) is exactly the gap Saga fills.

spranab 8mo ago
[dead]
westurner 8mo ago
Np. Still learning about how to reference agentskills from my coding LLM here. Haven't yet worked with MCP or A2A much. I should port instruction prompts from each project's AGENTS.md to reusable skills in .claude/skills/skillname/SKILL.md ; so that I can reference "/skillname" in a prompt to reuse that prompt text. Copilot looks for agent skills in .claude/skills/, .github/skills/, or .agents/skills/ . FWIU there are package managers for agent skills? PAKS: https://github.com/stakpak/paks https://github.com/stakpak/paks vercel-labs/skills: https://github.com/vercel-labs/skills https://github.com/vercel-labs/skills : export DISABLE_TELEMETRY=1 npx skills add vercel-labs/agent-skills https://skills.sh/docs/cli https://skills.sh/docs/cli A reproducibility, traceability, and auditability concern: How to show the text of the exact version of the skill referenced in each input prompt in each chat log? GH copilot's JSON export format is a fairly complete trace compared to e.g. Gemini Chat. . ElasticSearch (Java) and Meilisearch (Rust) do at least porter stemming before indexing. MeiliSearch can be used as a vector database for langchain. meilisearch/meilisearch: https://github.com/meilisearch/meilisearch https://github.com/meilisearch/meilisearch : Meilisearch docs > langchain guide > Performing Similarity Search: https://www.meilisearch.com/docs/guides/langchain#performing-similarity-search https://www.meilisearch.com/docs/guides/langchain#performing... from langchain.vectorstores import Meilisearch from langchain.embeddings.openai import OpenAIEmbeddings meilisearch/meilisearch-mcp : https://github.com/meilisearch/meilisearch-mcp https://github.com/meilisearch/meilisearch-mcp openobserve/openobserve : https://github.com/openobserve/openobserve https://github.com/openobserve/openobserve : > OpenObserve is an open-source observability platform for logs, metrics, traces, and frontend monitoring. A cost-effective alternative to Datadog, Splunk, and Elasticsearch with 140x lower storage costs and single binary deployment. with SQL in Rust OpenObserve (AGPL) docs > MCP: https://openobserve.ai/docs/integration/mcp/ https://openobserve.ai/docs/integration/mcp/ : > This capability is supported in the Enterprise edition of OpenObserve https://github.com/openobserve/openobserve/issues/6804 https://github.com/openobserve/openobserve/issues/6804 openobserve docs > Comparison with Alternatives > How does OpenObserve compare to Elasticsearch: https://openobserve.ai/docs/overview/comparison/ https://openobserve.ai/docs/overview/comparison/ meilisearch blog > "What is GraphRAG: Complete guide [2026]" https://www.meilisearch.com/blog/graph-rag https://www.meilisearch.com/blog/graph-rag : > How is GraphRAG different from baseline RAG? > GraphRAG represents one of the different types of RAG, and its retrieval process differs from baseline RAG. > Baseline RAG is vector search-based, while GraphRAG uses structured relationships to get the end result. GraphRAG can still use vector and full-text search, but relationships drive what gets retrieved. /? graphrag sqlite fts : https://www.google.com/search?q=graphrag+sqlite+fts https://www.google.com/search?q=graphrag+sqlite+fts